SpringBoot集成Hadoop系列二 ---- MapReduce一年最高气温统计

本是古典何须时尚 2022-01-28 09:17 430阅读 0赞

### 代码: ###

package com.hadoop.reduce.mapper;
    
    import java.io.IOException;
    import java.util.StringTokenizer;
    
    import org.apache.hadoop.io.IntWritable;
    import org.apache.hadoop.io.LongWritable;
    import org.apache.hadoop.io.Text;
    import org.apache.hadoop.mapred.MapReduceBase;
    import org.apache.hadoop.mapred.Mapper;
    import org.apache.hadoop.mapred.OutputCollector;
    import org.apache.hadoop.mapred.Reporter;
    
    /**
     * 读取一年中某天的最高气温
     * @author linhaiy
     * @date 2019.05.18
     */
    public class WeatherMap extends MapReduceBase implements Mapper<LongWritable, Text, Text, LongWritable> {
    
    	private Text word = new Text();
    
    	public void map(LongWritable key, Text value, OutputCollector<Text, LongWritable> output, Reporter reporter)
    			throws IOException {
    		// 打印输入样本 如 2018120715
    		System.out.println("==== Before Mapper: ====" + key + "," + value);
    		String line = value.toString();
    		StringTokenizer tokenizer = new StringTokenizer(line);
    		// 截取年份
    		String year = line.substring(0, 4);
    		// 截取温度
    		int temperature = Integer.parseInt(line.substring(8));
    		word.set(year);
    		output.collect(word, new LongWritable(temperature));
    
    		// 打印输出样本
    		System.out.println("==== After Mapper: ==== " + new Text(year) + "," + new LongWritable(temperature));
    	}
    }

package com.hadoop.reduce.reducer;
    
    import java.io.IOException;
    import java.util.Iterator;
    
    import org.apache.hadoop.io.IntWritable;
    import org.apache.hadoop.io.LongWritable;
    import org.apache.hadoop.io.Text;
    import org.apache.hadoop.mapred.MapReduceBase;
    import org.apache.hadoop.mapred.OutputCollector;
    import org.apache.hadoop.mapred.Reducer;
    import org.apache.hadoop.mapred.Reporter;
    
    /**
     * 统计一年天气最高温
     * @author linhaiy
     * @date 2019.05.18
     */
    public class WeatherReduce extends MapReduceBase implements Reducer<Text, LongWritable, Text, LongWritable> {
    
    	@Override
    	public void reduce(Text key, Iterator<LongWritable> values, OutputCollector<Text, LongWritable> output,
    			Reporter reporter) throws IOException {
    		long maxValue = Integer.MIN_VALUE;
    		StringBuffer sb = new StringBuffer();
    
    		// 取values温度的最大值
    		while (values.hasNext()) {
    			long tmp = values.next().get();
    			maxValue = Math.max(maxValue, tmp);
    			sb.append(tmp).append(", ");
    
    			output.collect(key, new LongWritable(maxValue));
    		}
    
    		// 打印输入样本，如 2000， 15 ，99， 12
    		System.out.println("==== Before Reduce ==== " + key + ", " + sb.toString());
    		// 打印输出样本
    		System.out.println("==== After Reduce ==== " + key + ", " + sb.toString());
    	}
    
    }

package com.hadoop.reduce.service;
    
    import java.io.IOException;
    
    import javax.annotation.PostConstruct;
    
    import org.apache.hadoop.conf.Configuration;
    import org.apache.hadoop.fs.Path;
    import org.apache.hadoop.io.IntWritable;
    import org.apache.hadoop.io.LongWritable;
    import org.apache.hadoop.io.NullWritable;
    import org.apache.hadoop.io.Text;
    import org.apache.hadoop.mapred.JobConf;
    import org.apache.hadoop.mapred.TextInputFormat;
    import org.apache.hadoop.mapred.TextOutputFormat;
    import org.apache.hadoop.mapreduce.Job;
    import org.apache.hadoop.mapreduce.lib.input.CombineTextInputFormat;
    import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
    import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
    import org.apache.hadoop.mapreduce.lib.output.MultipleOutputs;
    import org.springframework.beans.factory.annotation.Value;
    import org.springframework.stereotype.Component;
    
    import com.hadoop.reduce.bean.StaffProvincePartitioner;
    import com.hadoop.reduce.bean.WeiboInputFormat;
    import com.hadoop.reduce.mapper.CounterMapper;
    import com.hadoop.reduce.mapper.FriendsMapper;
    import com.hadoop.reduce.mapper.JoinMapper;
    import com.hadoop.reduce.mapper.StaffMap;
    import com.hadoop.reduce.mapper.WeatherMap;
    import com.hadoop.reduce.mapper.WeiboMapper;
    import com.hadoop.reduce.mapper.WordCount;
    import com.hadoop.reduce.mapper.WordCountMap;
    import com.hadoop.reduce.model.GroupSortModel;
    import com.hadoop.reduce.model.OrderInfo;
    import com.hadoop.reduce.model.StaffModel;
    import com.hadoop.reduce.model.Weibo;
    import com.hadoop.reduce.reducer.FriendsReduce;
    import com.hadoop.reduce.reducer.JoinReduce;
    import com.hadoop.reduce.reducer.StaffReduce;
    import com.hadoop.reduce.reducer.WeatherReduce;
    import com.hadoop.reduce.reducer.WeiboReduce;
    import com.hadoop.reduce.reducer.WordCountReduce;
    import com.hadoop.util.GroupSort;
    
    /**
     * Map/Reduce工具类
     * 
     * @author linhaiy
     * @date 2019.05.18
     */
    @Component
    public class ReduceJobsUtils {
    
    	@Value("${hdfs.path}")
    	private String path;
    
    	private static String hdfsPath;
    
    	/**
    	 * 获取HDFS配置信息
    	 * @return
    	 */
    	public static Configuration getConfiguration() {
    		Configuration configuration = new Configuration();
    		configuration.set("fs.defaultFS", hdfsPath);
    		configuration.set("mapred.job.tracker", hdfsPath);
    		// 运行在yarn的集群模式
    		// configuration.set("mapreduce.framework.name", "yarn");
    		// 这个配置是让main方法寻找该机器的mr环境
    		// configuration.set("yarn.resourcemanmager.hostname", "node1");
    		return configuration;
    	}
    
    	/**
    	 * 获取单词一年最高气温计算配置
    	 * @param jobName
    	 * @return
    	 */
    	public static JobConf getWeatherJobsConf(String jobName) {
    		JobConf jobConf = new JobConf(getConfiguration());
    		jobConf.setJobName(jobName);
    		jobConf.setOutputKeyClass(Text.class);
    		jobConf.setOutputValueClass(LongWritable.class);
    		jobConf.setMapperClass(WeatherMap.class);
    		jobConf.setReducerClass(WeatherReduce.class);
    		jobConf.setInputFormat(TextInputFormat.class);
    		jobConf.setOutputFormat(TextOutputFormat.class);
    		return jobConf;
    	}
    
    	@PostConstruct
    	public void getPath() {
    		hdfsPath = this.path;
    	}
    
    	public static String getHdfsPath() {
    		return hdfsPath;
    	}
    }

package com.hadoop.reduce.service;
    
    import org.apache.commons.lang.StringUtils;
    import org.apache.hadoop.fs.Path;
    import org.apache.hadoop.mapred.FileInputFormat;
    import org.apache.hadoop.mapred.FileOutputFormat;
    import org.apache.hadoop.mapred.JobClient;
    import org.apache.hadoop.mapred.JobConf;
    import org.springframework.stereotype.Service;
    import com.hadoop.hdfs.service.HdfsService;
    
    /**
     * 单词统计
     * @author linhaiy
     * @date 2019.05.18
     */
    @Service
    public class MapReduceService {
    
    	// 默认reduce输出目录
    	private static final String OUTPUT_PATH = "/output";
    
    	/**
    	 * 一年最高气温统计
    	 * @param jobName
    	 * @param inputPath
    	 * @throws Exception
    	 */
    	public void weather(String jobName, String inputPath) throws Exception {
    		if (StringUtils.isEmpty(jobName) || StringUtils.isEmpty(inputPath)) {
    			return;
    		}
    		// 输出目录 = output/当前Job
    		String outputPath = OUTPUT_PATH + "/" + jobName;
    		if (HdfsService.existFile(outputPath)) {
    			HdfsService.deleteFile(outputPath);
    		}
    		JobConf jobConf = ReduceJobsUtils.getWeatherJobsConf(jobName);
    		FileInputFormat.setInputPaths(jobConf, new Path(inputPath));
    		FileOutputFormat.setOutputPath(jobConf, new Path(outputPath));
    		JobClient.runJob(jobConf);
    	}
    }

package com.hadoop.reduce.controller;
    
    import org.apache.commons.lang.StringUtils;
    import org.springframework.beans.factory.annotation.Autowired;
    import org.springframework.web.bind.annotation.RequestMapping;
    import org.springframework.web.bind.annotation.RequestMethod;
    import org.springframework.web.bind.annotation.RequestParam;
    import org.springframework.web.bind.annotation.ResponseBody;
    import org.springframework.web.bind.annotation.RestController;
    import com.hadoop.reduce.service.MapReduceService;
    import com.hadoop.util.Result;
    
    /**
     * MapReduce处理控制层
     * @author linhaiy
     * @date 2019.05.18
     */
    @RestController
    @RequestMapping("/hadoop/reduce")
    public class MapReduceAction {
    
    	@Autowired
    	MapReduceService mapReduceService;
    
    	/**
    	 * 一年最高气温统计
    	 * @param jobName
    	 * @param inputPath
    	 * @return
    	 * @throws Exception
    	 */
    	@RequestMapping(value = "MaxWeather", method = RequestMethod.POST)
    	@ResponseBody
    	public Result weather(@RequestParam("jobName") String jobName, @RequestParam("inputPath") String inputPath)
    			throws Exception {
    		if (StringUtils.isEmpty(jobName) || StringUtils.isEmpty(inputPath)) {
    			return new Result(Result.FAILURE, "请求参数为空");
    		}
    		mapReduceService.weather(jobName, inputPath);
    		return new Result(Result.SUCCESS, "温度统计成功");
    	}
    }

### 部分截图: ###

![20190523200527802.png][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2xpbmhhaXl1bl95dGR4_size_16_color_FFFFFF_t_70][]

[20190523200527802.png]: /images/20220128/8896cd88413a438a98dc5fa561968ea7.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2xpbmhhaXl1bl95dGR4_size_16_color_FFFFFF_t_70]: /images/20220128/8e20a0eb244a4bb2ae0d83ec015c02ca.png